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Record W4392577697 · doi:10.29007/mg23

Mechanical vs Arithmetic Definitions of Coronal Plane Alignment of the Knee (CPAK) Measures Have Different Distributions: An Assessment of 3947 Cases

2024· article· en· W4392577697 on OpenAlexaff
Matthew Hickey, Asim Ali Khan, Joseph Baines, David J. Allen, Findlay Welsh, Kamal Deep, Alistair Ewen, François Leitner, Antony J. Hodgson, F. Picard

Bibliographic record

VenueEPiC series in health sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoronal planeTotal knee arthroplastyMedicineOrthodonticsKinematicsArthroplastyAnkleComputer scienceSurgeryRadiologyPhysics

Abstract

fetched live from OpenAlex

The Coronal Plane Alignment of the Knee (CPAK) classification has been used to describe healthy and arthritic knee alignment as well as to predict phenotypes which could benefit from kinematic alignment using soft tissue balancing during TKA. At our institution, we have access to a large database of navigated TKA procedures including intra and postoperative mechanical hip-knee-ankle angle (mHKA) measurements, which are defined differently than the aHKA. It has been previously recognized that these alternative, but related, measures of coronal alignment may have different distributions. The primary aim of this study was therefore to determine if the CPAK classification frequencies described in the original publication by MacDessi et al. for the aHKA are similar to frequencies acquired using the mHKA. A secondary aim was to categorise postoperative TKA alignment at our institution utilising the mHKA-based CPAK classification. We analysed data from 3947 total knee arthroplasty procedures undertaken using surgical navigation at our institution between March 2007 and October 2022. The mHKA was measured directly during the registration process while JLO was calculated using the mHKA and LDFA (JLO = HKA + 2xLDFA). This was completed twice for each case using the pre and postoperative mHKA and LDFA. Each case was then categorized as one of the nine CPAK phenotypes. The pre-operative mean mHKA was 2.00 varus using surgical navigation (compared to 0.80 varus reported by Macdessi et al. using the aHKA). The pre-operative mean JLO was 1750 (versus 1740). Using the mHKA as opposed to the aHKA resulted in more knees being categorized as Class I (34.0% vs 19.4% ) or Class IV (17.5% vs 19.8%) and fewer in Class II (19.0% vs 32.2%) and Class V (6.3% vs 14.6%). All other differences in class frequencies were within 4%. For postoperative CPAK classification, a large majority of knees (72.7%) were categorized as Class V. Our study using mHKA determined during navigated TKA showed that the majority of preoperative arthritic knees were Class I, II, and IV in contrast to the original CPAK publication where most preoperative knees were Class I, II, and III. For TKAs at our institution, the goal was to mechanically align knees to neutral mHKA and JLO. This reflects in our postoperative results in that 73% of all postoperative TKAs were categorized as Class V.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.386
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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